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#221
Jakob

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I might potentially try for an internship there in the summer of 2021.


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#222
Alislaws

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I might potentially try for an internship there in the summer of 2021.

At Deep Mind, or in the US Military?  :biggrin:

 

I was all "Cool you'll be coming to London!" but turns out you might have other options:

 

DeepMind in London: DeepMind was founded in London in 2010 and the city is home to the majority of our research, applied and operations teams, as well as our founders.

DeepMind Alberta: led by Rich Sutton, Mike Bowling and Patrick Pilarski, the Alberta team focuses on core AI research towards to goal of solving intelligence and was our first international research lab to open. It is located in Edmonton, Canada and maintains close ties with the University of Alberta.

DeepMind Montreal: led by Doina Precup. The team also focuses on core AI research towards the goal of solving intelligence and works closely with McGill University.

DeepMind in Mountain View: we have a growing applied team in Mountain View, CA, which focuses on real-world applications of our research.

DeepMind Paris: Our first research lab in continental Europe will open later in 2018. Led by one of DeepMind's principal research scientists, Remi Munos, the team will focus on fundamental AI research, building on Remi’s previous scientific contributions.

 

Looks like London is a Mix of everything, Mountain View CA is their practical applications lab and all the others are fundamental AI research places linked to universities? 

 

Which would you prefer?

 

EDIT: If you do go work for them at some point, you should write out a bunch of AI predictions first, then look at them again after a year in the thick of it and see how much you'd change them, it would be interesting!


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#223
Yuli Ban

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I wonder if there is a military out there (maybe the US military) has already utilized an AI general managing some aspects of today's battlefield? Maybe it's already going through trial runs?

AI today really just isn't good enough yet. Every system lacks real-world generalized commonsense, and that would just lead to disaster if a military used it. 

 

Now using an AI to parse the data drawn from a battlefield scan? I can imagine that being used.


And remember my friend, future events such as these will affect you in the future.


#224
Jakob

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I might potentially try for an internship there in the summer of 2021.

At Deep Mind, or in the US Military?  :biggrin:

 

I was all "Cool you'll be coming to London!" but turns out you might have other options:

 

DeepMind in London: DeepMind was founded in London in 2010 and the city is home to the majority of our research, applied and operations teams, as well as our founders.

DeepMind Alberta: led by Rich Sutton, Mike Bowling and Patrick Pilarski, the Alberta team focuses on core AI research towards to goal of solving intelligence and was our first international research lab to open. It is located in Edmonton, Canada and maintains close ties with the University of Alberta.

DeepMind Montreal: led by Doina Precup. The team also focuses on core AI research towards the goal of solving intelligence and works closely with McGill University.

DeepMind in Mountain View: we have a growing applied team in Mountain View, CA, which focuses on real-world applications of our research.

DeepMind Paris: Our first research lab in continental Europe will open later in 2018. Led by one of DeepMind's principal research scientists, Remi Munos, the team will focus on fundamental AI research, building on Remi’s previous scientific contributions.

 

Looks like London is a Mix of everything, Mountain View CA is their practical applications lab and all the others are fundamental AI research places linked to universities? 

 

Which would you prefer?

 

EDIT: If you do go work for them at some point, you should write out a bunch of AI predictions first, then look at them again after a year in the thick of it and see how much you'd change them, it would be interesting!

 

LOL I meant DeepMind. I was thinking either Mountain View or Montreal. But I'm not sure if I'd get to pick the location for internships. Maybe you just apply to the company and they send you wherever you're needed.


Edited by Jakob, 12 July 2019 - 02:29 AM.


#225
Zaphod

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Deepmind produced an 8-part podcast series with Dr Hannah Fry presenting:

 

https://deepmind.com...eepmind-podcast

 

https://podcasts.goo...S9KVDZwYlBrZw==


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#226
Yuli Ban

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DeepMind’s AI can apply learned knowledge to complete novel tasks

Can AI agents learn to generalize beyond its immediate experience? That’s an open question in machine learning research, and an area of acute interest for firms like Google parent company Alphabet’s DeepMind.
In a study conducted in collaboration with Stanford and the University College London, DeepMind scientists investigated whether systems could apply the knowledge they’d learned in one task to other, tangentially related tasks. They report that in environments ranging from a grid-world to an interactive 3D room generated in Unity (a game engine), their AI-driven agents correctly exploited the “compositional nature” of a language to interpret never-seen-before instructions.

Emergent Systematic Generalization in a Situated Agent


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#227
wjfox

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DeepMind’s AI has now outcompeted nearly all human players at StarCraft II
 
In January of this year, DeepMind announced it had hit a milestone in its quest for artificial general intelligence. It had designed an AI system, called AlphaStar, that beat two professional players at StarCraft II, a popular video game about galactic warfare. This was quite a feat. StarCaft II is highly complex, with 1026 choices for every move. It’s also a game of imperfect information—and there are no definitive strategies for winning. The achievement marked a new level of machine intelligence.
 
Now DeepMind, an Alphabet subsidiary, is releasing an update. AlphaStar now outranks the vast majority of active StarCraft players, demonstrating a much more robust and repeatable ability to strategize on the fly than before. The results, published in Nature today, could have important implications for applications ranging from machine translation to digital assistants or even military planning.
 
StarCraft II is a real-time strategy game, most often played one on one. A player must choose one of three human or alien races—Protoss, Terran, or Zerg—and alternate between gathering resources, building infrastructure and weapons, and attacking the opponent to win the game. Every race has unique skill sets and limitations that affect the winning strategy, so players commonly pick and master playing with one.
 
AlphaStar used reinforcement learning, where an algorithm learns through trial and error, to master playing with all the races. “This is really important because it means that the same type of methods can in principle be applied to other domains,” said David Silver, DeepMind’s principal research scientist, on a press call. The AI also reached a rank above 99.8% of the active players in the official online league.
 
 
 
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#228
Yuli Ban

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DeepMind co-founder moves to Google as the AI lab positions itself for the future

Mustafa Suleyman previously led DeepMind’s health team

The personnel changes at Alphabet continue, this time with Mustafa Suleyman — one of the three co-founders of the company’s influential AI lab DeepMind — moving to Google.
Suleyman announced the news on Twitter, saying that after a “wonderful decade” at DeepMind, he would be joining Google to work with the company’s head of AI Jeff Dean and its chief legal officer Kent Walker. The exact details of Suleyman’s new role are unclear but a representative for the company told The Verge it would involve work on AI policy.
The move is notable, though, as it was reported earlier this year that Suleyman had been placed on leave from DeepMind. (DeepMind disputed these reports, saying it was a mutual decision intended to give Suleyman “time out ... after 10 hectic years.”) Some speculated that Suleyman’s move was the fallout of reported tensions between DeepMind and Google, as the former struggled to commercialize its technology.


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#229
Yuli Ban

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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.


And remember my friend, future events such as these will affect you in the future.






Also tagged with one or more of these keywords: DeepMind, deep learning, deep reinforcement learning, progressive neural network, artificial intelligence, AGI, differentiable neural, Google, RankBrain, artificial neural network

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